A method of generating soft decision detection parameters for a plurality of received signals. The method comprises defining a hard decision boundary and a plurality of quantisation intervals wherein each quantisation interval extends from the hard decision boundary by an interval distance, selecting a log likelihood value from a set of log likelihood values for each received signal based on the quantisation interval in which the received signal is detected, performing a soft decoding using a plurality of log likelihood values, adjusting the set of log likelihood values based on a result of the soft decoding, determining an error probability for a quantisation interval, comparing the error probability against a target error probability and adjusting the interval distance in order to obtain the target error probability.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A method of generating soft decision detection parameters for a plurality of received signals, the method performed by a device in a signal receiver, the method comprising: defining a hard decision boundary and a plurality of quantisation intervals wherein each quantisation interval extends from the hard decision boundary by an interval distance; selecting a log likelihood value from a set of log likelihood values for each received signal based on the quantisation interval in which the received signal is detected; performing a soft decoding using a plurality of log likelihood values; adjusting the set of log likelihood values based on a result of the soft decoding; determining an error probability for a quantisation interval; comparing the error probability against a target error probability; and adjusting the interval distance in order to obtain the target error probability, wherein the signal receiver is configured to generate soft decision information for a signal using the hard decision boundary and the interval distance determined by the device.
2. The method according to claim 1 , wherein the error probability for a quantisation interval is determined using a log likelihood value from the adjusted set of log likelihood values.
3. The method according to claim 2 , further comprising: adjusting the hard decision boundary by: calculating a fraction of the plurality of received signals that have a detected value which is less than the hard decision boundary; determining whether the fraction of the received signals is less than a threshold; and modifying the hard decision boundary by: reducing the hard decision boundary when the fraction is greater than the threshold; and increasing the hard decision boundary when the fraction is less than the threshold.
4. The method according to claim 3 , wherein the threshold equals an expected number of received signals with a detected value which is less than the hard decision boundary relative to a total number of received signals.
5. The method according to claim 4 , wherein adjusting the interval distance in order to obtain a target error probability further comprises: increasing the interval distance when the error probability is greater than the target error probability; and decreasing the interval distance when the error probability is less than the target error probability.
6. The method according to claim 5 wherein the plurality of quantisation intervals comprises: a first quantisation interval that extends to a first soft decision boundary wherein the first soft decision boundary is less than the hard decision boundary; and a second quantisation interval that extends to a second soft decision boundary wherein the second soft decision boundary is greater than the hard decision boundary.
7. The method according to claim 6 , wherein the method is repeated for each hard decision boundary of a number of hard decision boundaries, said number equaling a total number of system states minus one.
8. The method according to claim 7 , further comprising: adjusting the set of log likelihood values based on the adjusted interval distance.
9. The method according to claim 8 , wherein the method is repeated after a predetermined number of signals have been received.
10. A signal receiver comprising a device for generating soft decision detection parameters for a plurality of received signals comprising a processor and a memory storing instructions for execution by the processor, the instructions causing the processor when executing the instructions to: define a hard decision boundary and a plurality of quantisation intervals wherein each quantisation interval extends from the hard decision boundary by an interval distance; select a log likelihood value from a set of log likelihood values for each received signal based on the quantisation interval in which the received signal is detected; perform a soft decoding using a plurality of log likelihood values; adjust the set of log likelihood values based on a result of the soft decoding; determine an error probability for a quantisation interval; compare the error probability against a target error probability; and adjust the interval distance in order to obtain the target error probability, wherein the signal receiver is configured to generate soft decision information for a signal using the hard decision boundary and the interval distance determined by the device.
11. The device of claim 10 , wherein the error probability for a quantisation interval is determined using a log likelihood value from the adjusted set of log likelihood values.
12. The device of claim 11 , wherein the instructions further cause the processor when executing the instructions to: adjust the hard decision boundary by: calculating a fraction of the plurality of received signals that have a detected value which is less than the hard decision boundary; determining whether the fraction of the received signals is less than a threshold; and modifying the hard decision boundary by: reducing the hard decision boundary when the fraction is greater than the threshold; and increasing the hard decision boundary when the fraction is less than the threshold.
13. The device of claim 12 , wherein the threshold equals an expected number of received signals with a detected value which is less than the hard decision boundary relative to a total number of received signals.
14. The device of claim 13 , wherein adjusting the interval distance in order to obtain a target error probability further comprises: increasing the interval distance when the error probability is greater than the target error probability; and decreasing the interval distance when the error probability is less than the target error probability.
15. The device of claim 14 , wherein the plurality of quantisation intervals comprises: a first quantisation interval that extends to a first soft decision boundary wherein the first soft decision boundary is less than the hard decision boundary; and a second quantisation interval that extends to a second soft decision boundary wherein the second soft decision boundary is greater than the hard decision boundary.
16. The device of claim 15 , wherein the instructions are repeated for each hard decision boundary of a number of hard decision boundaries, said number equalling a total number of system states minus one.
17. The device of claim 16 , wherein the instructions further cause the processor when executing the instructions to: adjust the set of log likelihood values based on the adjusted interval distance.
18. The device of claim 17 , wherein the instructions are repeated after a predetermined number of signals has been received.
19. A method of generating soft decision detection parameters for a plurality of received signals, the method performed by a device in a flash memory comprising a plurality of flash memory cells, the method comprising: defining a hard decision boundary and a plurality of quantisation intervals wherein each quantisation interval extends from the hard decision boundary by an interval distance; selecting a log likelihood value from a set of log likelihood values for each received signal based on the quantisation interval in which the received signal is detected; performing a soft decoding using a plurality of log likelihood values; adjusting the set of likelihood values based on a result of the soft decoding; determining an error probability for a quantisation interval; comparing the error probability against a target error probability; and adjusting the interval distance in order to obtain the target error probability, wherein the flash memory is configured to read a flash memory cell and generate soft decision information for the flash memory cell using the hard decision boundary and the interval distance determined by the device.
20. A flash memory comprising a plurality of flash memory cells and a device for generating soft decision detection parameters for a plurality of received signals comprising a processor and a memory storing instructions for execution by the processor, the instructions causing the processor when executing the instructions to: define a hard decision boundary and a plurality of quantisation intervals wherein each quantisation interval extends from the hard decision boundary by an interval distance; select a log likelihood value from a set of log likelihood values for each received signal based on the quantisation interval in which the received signal is detected: perform a soft decoding using a plurality of log likelihood values; adjust the set of likelihood values based on a result of the soft decoding; determine an error probability for a quantisation interval; compare the error probability against a target error probability; and adjust the interval distance in order to obtain the target error probability, wherein the flash memory is configured to read a flash memory cell and generate soft decision information for the flash memory cell using the hard decision boundary and the interval distance determined by the device.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
February 5, 2019
January 26, 2021
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.